Monthly Archives: August 2017

The Floyd Warshall algorithm is an all pairs shortest paths algorithm. This can be contrasted with algorithms like Dijkstra’s which give the shortest paths from a single node to all other nodes in the graph.

Floyd Warshall’s algorithm works by considering first the edge set of the graph. This is the set of all paths of the graph through one edge. Node pairs that are connected to one another through an edge will have their shortest path set to the length of that edge, while all other node pairs will have their shortest path set to infinity. The program then runs through every triplet of nodes (i, j, k) and checks if the path from i to k and the path from k to j is shorter than the current path from i to j. If so, then the distance and the path is updated.

So lets consider an example on the graph in the image above. The edge set of this graph is E = {(0, 1), (0, 2), (0, 3), (1, 3), (3, 4)}. So our initial table is:

0

1

2

3

4

0

inf

(0, 1)

(0, 2)

(0, 3)

inf

1

(0, 1)

inf

inf

(1, 3)

inf

2

(0, 2)

inf

inf

inf

inf

3

(0, 3)

(1, 3)

inf

inf

(3, 4)

4

inf

inf

inf

(3, 4)

inf

As we look to update the paths, we first look for routes that go through node 0:

Because node 0 connects to both node 1 and node 2, but node 1 does not connect to node 2, we have the following truth holding in the matrix above: cost(0, 1) + cost(0, 2) < cost(1, 2), so we can update the shortest path from node 1 to node 2 to be (1, 0, 2).

Because node 0 connects to both node 2 and node 3, but node 2 does not connect to node 3, we have the following truth holding in the matrix above: cost(0, 2) + cost(0, 3) < cost(2, 3), so we can update the shortest path from node 2 to node 3 to be (2, 0, 3).

Because node 3 connects to both node 0 and node 4, but node 0 does not connect to node 4, we have the following truth holding in the matrix above: cost(0, 3) + cost(3, 4) < cost(0, 4), so we can update the shortest path from node 0 to node 4 to be (0, 3, 4).

Because node 3 connects to both node 1 and node 4, but node 1 does not connect to node 4, we have the following truth holding in the matrix above: cost(1, 3) + cost(3, 4) < cost(1, 4), so we can update the shortest path from node 1 to node 4 to be (1, 3, 4).

Because node 3 connects to both node 2 and node 4, but node 2 does not connect to node 4, we have the following truth now holding: cost(2, 3) + cost(3, 4) < cost(2, 4), so we can update the shortest path from node 2 to node 4 to be (2, 0, 3, 4).

The final table giving the list of shortest paths from every node to every other node is given below.

I wanted to spend some time on centrality measures of a graph. These are measurements of how important each node (or edge) is to the overall graph. But how do we define, or determine, importance? There is no unique way to answer this question, so there are varying metrics for measuring centrality. Which one you choose depends on several factors including how many other nodes of the graph are included, as well as the run time of the metrics you’re considering.

I have just published a script focusing on the degree centrality metric. The degree centrality metric is called a “walk metric” because it determines how important a node is by how many other nodes that can be reached by walks of up to a certain length. Lets look at the definition of the degree of a node to see if we can understand why it is called a walk metric.

In an undirected graph G = (V, E), the degree of a node u [in] V is the |{v | (u, v) [in] E}|. This is the size of the set of nodes that are connected to node u via a single edge. Another way of describing a single edge is a walk of length one. So the degree metric measures the importance of a node by the number of unique walks of length one.

The normalized degree centrality of a node v in a graph G = (V,E) measures how many nodes are connected to the node v, compared to the maximum possible number of edges that can be connected to this node. Because we are dealing with simple undirected graphs (at most a single edge between any two distinct vertices), this maximum possible number will always be |V – 1|. So the normalized degree can be calculated by dividing the degree of the node (the number of nodes it is connected to) by |V – 1|.

So for the example above, the node 0 has degree 6 because it is connected to nodes 2, 5, 9, 10, 11, and 12. There are 15 total nodes in this graph, so to calculate the normalized degree centrality of the node 0, it will be 6 / 14, which rounds to 0.428571.